基于机载LiDAR的高郁闭度华北落叶松林单木识别

  目的  高郁闭度华北落叶松林Larix principis-rupprechtii林木树冠交叉重叠，传统的基于高分辨影像的单木识别方法识别精度不高。利用机载LiDAR三维点云数据可提高高郁闭度华北落叶松林的单木识别精度。  方法  在点云数据预处理基础上，提出基于点云空间特征的高斯核函数改进的均值漂移单木位置识别方法(MSP)，比较并分析MSP法与基于点云空间特征的区域生长点云分割方法(RGP)、基于冠层高度模型的局部最大值单木位置识别方法(LMC)和基于冠层模型的多尺度分割单木位置识别方法(MSC)的单木识别效果。  结果  4种方法单木位置识别精度从大到小依次为MSP (89.30%)、LMC (85.60%)、RGP (77.50%)和MSC (70.00%)，MSP的漏分误差和错分误差最小，分别为8.7%和8.0%，平均单木冠幅提取精度为90.18%。  结论  提出的MSP法对高郁闭度华北落叶松林单木位置识别具有较好的适用性，利用机载LiDAR可为提取华北落叶松林森林结构参数提供新的途径。图3表3参28

Individual tree detection in the high canopy density Larix principis-rupprechtii forest based on airborne LiDAR

  Objective  With low identification accuracy of individual trees in Larix principis-rupprechtii forest with high canopy density employing high resolution images, this paper is aimed to confirm the strengths of airborne Laser Detection and Ranging (LiDAR) 3D point cloud data as an alternative with a workable method proposed.   Method  Based on the preprocessing of point cloud data, an improved Mean Shift with Gaussian kernel function (MSP) position recognition method on the basis of the spatial characteristics of airborne LiDAR point cloud was proposed. The comparison is made with other three commonly used methods: regional growing segmentation algorithm based on point cloud (RGP), local maximum method based on canopy height model (LMC) and multi-scale segmentation method based on CHM (MSC).   Result  Identification accuracy of the four methods is: MSP (89.30%)＞LMC (85.60%)＞RGP (77.50%)＞MSC (70.00%), and MSC, the proposed method, displayed high average individual tree crown extraction accuracy (90.18%) and relatively low omission error and commission error rate: 8.7% and 8.0% respectively.   Conclusion  The proposed MSP has good applicability in high crown density L. principis-rupprechtii forest and provides a new way of extracting L. principis-rupprechtii forest structure parameters accurately on the basis of airborne LiDAR point clouds. Ch, 3 fig. 3 tab. 28 ref.]